Figures for “Sources and consequences of mismatch between leaf disc and whole-leaf leaf mass per area (LMA)”
Masatoshi Katabuchi
May 02, 2022
library(tidyverse)
library(targets)
library(kableExtra)1 Leaf disc vs whole-leaf LMA (species-level)
1.1 Pooled
1.2 Seprated
1.3 MCMC check
targets::tar_load("fit_sp_0_summary_simple")
targets::tar_load("fit_sp_1_summary_model")
targets::tar_load("fit_sp_2_summary_model")
targets::tar_load("fit_sp_3_summary_model")
targets::tar_load("fit_sp_4_summary_sma")
# targets::tar_load("fit_sp_5_summary_sma")
targets::tar_load("fit_sp_6_summary_sma_err")
targets::tar_load("fit_sp_0_diagnostics_simple")
targets::tar_load("fit_sp_1_diagnostics_model")
targets::tar_load("fit_sp_2_diagnostics_model")
targets::tar_load("fit_sp_3_diagnostics_model")
targets::tar_load("fit_sp_4_diagnostics_sma")
# targets::tar_load("fit_sp_5_diagnostics_sma")
targets::tar_load("fit_sp_6_diagnostics_sma_err")fit_sp_0_summary_simple |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_1_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_2_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_3_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> # mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> # .join_data <dbl>
fit_sp_4_summary_sma |> filter(rhat > 1.1)
#> # A tibble: 29 × 11
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ 497. 428. 1.85e+2 1.38e+2 3.15e+2 9.00e+2 1.14 20.8
#> 2 sigma 0.0927 0.100 3.82e-2 4.46e-2 2.46e-2 1.41e-1 1.14 20.9
#> 3 sigma_x_un… 0.0386 0.0420 1.81e-2 2.09e-2 5.48e-3 6.13e-2 1.13 23.3
#> 4 log_lma_di… 4.33 4.32 1.47e-1 2.06e-1 4.12e+0 4.54e+0 1.10 28.1
#> 5 log_lma_di… 4.03 4.04 1.85e-1 2.62e-1 3.75e+0 4.28e+0 1.12 24.1
#> 6 log_lma_di… 3.97 3.97 1.82e-1 2.59e-1 3.70e+0 4.22e+0 1.10 28.9
#> 7 log_lma_di… 3.68 3.68 1.65e-1 2.35e-1 3.45e+0 3.93e+0 1.10 27.9
#> 8 log_lma_di… 3.93 3.93 1.58e-1 2.20e-1 3.71e+0 4.17e+0 1.12 25.0
#> 9 sigma_x 0.0967 0.105 4.52e-2 5.25e-2 1.37e-2 1.53e-1 1.13 23.3
#> 10 log_lik[12] -0.463 -0.574 1.88e+0 2.36e+0 -3.16e+0 2.51e+0 1.11 26.8
#> # … with 19 more rows, and 2 more variables: ess_tail <dbl>, .join_data <dbl>
# fit_sp_5_summary_sma |> filter(rhat > 1.1)
fit_sp_6_summary_sma_err |> filter(rhat > 1.1)
#> # A tibble: 2,305 × 11
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ 4.68e+2 4.86e+2 1.37e+2 1.73e+2 2.29e+2 630. 1.59 7.38
#> 2 beta[1] 9.18e-2 9.02e-2 6.09e-3 4.62e-3 8.25e-2 0.103 1.31 76.9
#> 3 beta[2] -3.03e-2 -2.95e-2 8.45e-3 8.71e-3 -4.57e-2 -0.0199 1.16 18.6
#> 4 beta[3] 3.19e-2 3.09e-2 6.73e-3 6.61e-3 2.23e-2 0.0431 1.13 20.6
#> 5 beta[4] 2.11e-3 1.84e-3 6.58e-3 4.64e-3 -9.85e-3 0.0133 1.47 123.
#> 6 gamma[1] -2.90e+0 -2.92e+0 3.96e-1 5.08e-1 -3.40e+0 -2.20 1.58 7.41
#> 7 gamma[2] -2.22e-1 -2.10e-1 1.22e-1 1.42e-1 -4.48e-1 -0.0897 1.31 10.6
#> 8 gamma[3] 4.30e-1 4.82e-1 1.80e-1 1.71e-1 1.25e-1 0.640 1.38 8.92
#> 9 gamma[4] -3.73e-1 -3.07e-1 2.76e-1 3.37e-1 -7.47e-1 -0.0291 1.53 7.37
#> 10 sigma_x_u… 3.55e-2 3.90e-2 9.91e-3 6.23e-3 1.27e-2 0.0439 1.52 7.49
#> # … with 2,295 more rows, and 2 more variables: ess_tail <dbl>,
#> # .join_data <dbl>It is difficult to model heteroskedasticity and measurement errors at the same time.
div_check(fit_sp_0_diagnostics_simple)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_1_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_2_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_3_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_4_diagnostics_sma)
#> [1] "645 of 16000 iterations ended with a divergence 4.03125 %"
# div_check(fit_sp_5_diagnostics_sma)
div_check(fit_sp_6_diagnostics_sma_err)
#> [1] "3169 of 8000 iterations ended with a divergence 39.6125 %"targets::tar_load(loo_model)
loo::loo_compare(loo_model[[1]], loo_model[[2]], loo_model[[3]], loo_model[[4]])
#> elpd_diff se_diff
#> model4 0.0 0.0
#> model3 -0.4 0.4
#> model1 -0.4 0.5
#> model2 -9.9 5.6tar_read(cv_sp)
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.4852 0.9108
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.007418 0.036529 0.028880
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.09834 0.06599 0.03733 0.04426
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 0.64625 0.88001 0.03052 0.100161.4 coef
tar_read(coef_sp_plot)
tar_load(coef_sp_png2)
tar_load(coef_sp_png3)
2 SMA table
2.1 species
tar_load(sma_sp_tab)
sma_sp_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| Thick~Large | 0.93 [0.85, 1.03] | 0.19 [0.01, 0.37] | 0.86 |
| Thick~Small | 1.02 [0.97, 1.07] | 0 [-0.11, 0.11] | 0.93 |
| Thin~Large | 0.91 [0.82, 1.02] | 0.2 [0.02, 0.37] | 0.71 |
| Thin~Small | 0.93 [0.83, 1.03] | 0.16 [-0.02, 0.35] | 0.84 |
2.2 species LD
tar_load(sma_sp_ld_tab)
sma_sp_ld_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.97 [0.94, 1.01] | 0.09 [0.02, 0.16] | 0.87 |
| Dense ~ Thick~Large | 0.96 [0.82, 1.13] | 0.14 [-0.15, 0.43] | 0.79 |
| Dense ~ Thick~Small | 1.02 [0.94, 1.11] | -0.01 [-0.17, 0.15] | 0.90 |
| Dense ~ Thin~Large | 0.8 [0.66, 0.98] | 0.37 [0.09, 0.64] | 0.60 |
| Dense ~ Thin~Small | 0.86 [0.72, 1.03] | 0.27 [-0.01, 0.55] | 0.81 |
| Nondense ~ Thick~Large | 0.75 [0.59, 0.94] | 0.58 [0.22, 0.94] | 0.72 |
| Nondense ~ Thick~Small | 0.99 [0.87, 1.13] | 0.05 [-0.23, 0.32] | 0.82 |
| Nondense ~ Thin~Large | 0.77 [0.65, 0.91] | 0.49 [0.25, 0.74] | 0.56 |
| Nondense ~ Thin~Small | 0.87 [0.71, 1.06] | 0.28 [-0.06, 0.62] | 0.68 |
2.3 tree
tar_load(sma_tree_tab)
sma_tree_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.96 [0.94, 0.99] | 0.11 [0.06, 0.16] | 0.72 |
| Thick~Large | 0.97 [0.9, 1.04] | 0.12 [-0.03, 0.26] | 0.61 |
| Thick~Small | 0.94 [0.9, 0.98] | 0.16 [0.07, 0.24] | 0.79 |
| Thin~Large | 0.91 [0.84, 0.97] | 0.21 [0.09, 0.32] | 0.45 |
| Thin~Small | 0.91 [0.85, 0.98] | 0.19 [0.07, 0.31] | 0.66 |
2.4 tree LD
tar_load(sma_tree_ld_tab)
sma_tree_ld_tab |>
kable() |>
kable_styling()| Data | Slope | Intercept | R2 |
|---|---|---|---|
| All | 0.96 [0.94, 0.99] | 0.11 [0.06, 0.16] | 0.72 |
| Dense ~ Thick~Large | 0.89 [0.79, 1] | 0.25 [0.06, 0.44] | 0.49 |
| Dense ~ Thick~Small | 0.91 [0.85, 0.97] | 0.21 [0.1, 0.32] | 0.76 |
| Dense ~ Thin~Large | 0.75 [0.67, 0.85] | 0.44 [0.29, 0.6] | 0.34 |
| Dense ~ Thin~Small | 0.8 [0.72, 0.9] | 0.36 [0.2, 0.52] | 0.65 |
| Nondense ~ Thick~Large | 0.74 [0.64, 0.86] | 0.61 [0.38, 0.83] | 0.43 |
| Nondense ~ Thick~Small | 0.84 [0.78, 0.91] | 0.38 [0.24, 0.53] | 0.68 |
| Nondense ~ Thin~Large | 0.73 [0.65, 0.81] | 0.57 [0.42, 0.72] | 0.28 |
| Nondense ~ Thin~Small | 0.89 [0.78, 1.01] | 0.25 [0.03, 0.46] | 0.38 |
3 Validation
tar_load(cv_sp)
cv_sp
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.4852 0.9108
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.007418 0.036529 0.028880
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.09834 0.06599 0.03733 0.04426
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 0.64625 0.88001 0.03052 0.10016tar_load(cv_tree)
cv_tree
#> $table
#> # A tibble: 4 × 3
#> model r2 mse
#> <chr> <dbl> <dbl>
#> 1 fit 1 0.718 0.0421
#> 2 fit 2 0.684 0.0467
#> 3 fit 3 0.702 0.0440
#> 4 fit 4 0.739 0.0385
#>
#> $fit1
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc)
#> 0.8911 0.8176
#>
#>
#> $fit2
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree,
#> offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(la) log(lt)
#> 0.05804 0.03071 0.05749
#>
#>
#> $fit3
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt),
#> data = tree, offset = log(lma_disc))
#>
#> Coefficients:
#> (Intercept) log(ld_leaf) log(la) log(lt)
#> 0.30044 0.16541 0.03573 0.11701
#>
#>
#> $fit4
#>
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt),
#> data = tree)
#>
#> Coefficients:
#> (Intercept) log(lma_disc) log(la) log(lt)
#> 1.54932 0.72303 0.01379 0.218864 Leaf disc vs whole-leaf LMA (individual-level)
5 Divergence (species-level)
6 LMA and LD (species-level)
7 CV
8 Leaf support cost (species-level)
9 Computing Environment
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.3 (2022-03-10)
#> os Ubuntu 20.04.4 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Etc/UTC
#> date 2022-05-02
#> pandoc 2.16.2 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> ! package * version date (UTC) lib source
#> P abind 1.4-5 2016-07-21 [?] RSPM (R 4.1.0)
#> P assertthat 0.2.1 2019-03-21 [?] RSPM (R 4.1.3)
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#> P base64url 1.4 2018-05-14 [?] RSPM (R 4.1.0)
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#> P bslib 0.3.1 2021-10-06 [?] RSPM (R 4.1.3)
#> P cachem 1.0.6 2021-08-19 [?] RSPM (R 4.1.0)
#> P callr 3.7.0 2021-04-20 [?] CRAN (R 4.1.1)
#> P car 3.0-12 2021-11-06 [?] RSPM (R 4.1.0)
#> P carData 3.0-5 2022-01-06 [?] RSPM (R 4.1.0)
#> P cellranger 1.1.0 2016-07-27 [?] RSPM (R 4.1.3)
#> P checkmate 2.0.0 2020-02-06 [?] RSPM (R 4.1.0)
#> P cli 3.2.0 2022-02-14 [?] RSPM (R 4.1.0)
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#> P DBI 1.1.2 2021-12-20 [?] RSPM (R 4.1.3)
#> P dbplyr 2.1.1 2021-04-06 [?] RSPM (R 4.1.3)
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